Article,
Physics-informed neural networks for one-dimensional sound field predictions with parameterized sources and impedance boundaries
Affiliations
- [1] Tech Univ Denmark, Dept Appl Math & Comp Sci, DK-2800 Lyngby, Denmark [NORA names: DTU Technical University of Denmark; University; Denmark; Europe, EU; Nordic; OECD]
Abstract
Realistic sound is essential in virtual environments, such as computer games and mixed reality. Efficient and accurate numerical methods for pre-calculating acoustics have been developed over the last decade; however, pre-calculating acoustics makes handling dynamic scenes with moving sources challenging, requiring intractable memory storage. A physics-informed neural network (PINN) method in one dimension is presented, which learns a compact and efficient surrogate model with parameterized moving Gaussian sources and impedance boundaries and satisfies a system of coupled equations. The model shows relative mean errors below 2%/0.2 dB and proposes a first step in developing PINNs for realistic three-dimensional scenes. (C) 2021 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).